Pushing automated morphological classifications to their limits with the Dark Energy Survey

TL;DR

This paper presents a deep learning approach to classify approximately 27 million galaxies from the Dark Energy Survey, achieving high accuracy and creating the largest automated galaxy morphology catalog to date.

Contribution

It introduces a CNN-based classification method trained on brighter galaxies and applied to fainter ones, significantly expanding automated galaxy morphology data.

Findings

97% accuracy on training set for galaxies brighter than magnitude 21.5

Secure classifications for about 87% of the catalog for ETG vs. LTG

High correlation with traditional morphological indicators like Sersic index and ellipticity

Abstract

We present morphological classifications of $\sim$27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs); and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have $\mathrm{m}_r \lesssim 17.7~\mathrm{mag}$; we model fainter objects to $\mathrm{m}_r < 21.5$ mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97\% accuracy to $\mathrm{m}_r<21.5$ on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for $\sim$ 87\% and 73\% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with S\'ersic index (\textit{n}), ellipticity ($\epsilon$) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.